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When Less Is More: Focused Pruning of Knowledge Bases to Improve Recognition of Student Conversation

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Intelligent Tutoring Systems (ITS 2012)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7315))

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Abstract

Expert knowledge bases are effective tools for providing a domain model from which intelligent, individualized support can be offered. This is even true for noisy data such as that gathered from activities involving ill-defined domains and collaboration. We attempt to automatically detect the subject of free-text collaborative input by matching students’ messages to an expert knowledge base. In particular, we describe experiments that analyze the effect of pruning a knowledge base to the nodes most relevant to current students’ tasks on the algorithm’s ability to identify the content of student chat. We discover a tradeoff. By constraining a knowledge base to its most relevant nodes, the algorithm detects student chat topics with more confidence, at the expense of overall accuracy. We suggest this trade-off be manipulated to best fit the intended use of the matching scheme in an intelligent tutor.

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© 2012 Springer-Verlag Berlin Heidelberg

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Floryan, M., Dragon, T., Woolf, B.P. (2012). When Less Is More: Focused Pruning of Knowledge Bases to Improve Recognition of Student Conversation. In: Cerri, S.A., Clancey, W.J., Papadourakis, G., Panourgia, K. (eds) Intelligent Tutoring Systems. ITS 2012. Lecture Notes in Computer Science, vol 7315. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30950-2_44

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  • DOI: https://doi.org/10.1007/978-3-642-30950-2_44

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-30949-6

  • Online ISBN: 978-3-642-30950-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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